AceCloud vs Beam

On-demand NVIDIA GPUs including A2, A30, L4, L40S, A100, H100, H200, RTX A6000, RTX 6000 Ada, RTX PRO 6000, and RTX 8000 for AI training, inference, GenAI, rendering, VFX, HPC, and data analytics

Published GPU instance pricing with monthly plans, longer-term commitment savings, and pay-as-you-go options with no hidden charges

Managed Kubernetes with GPU node groups, autoscaling clusters, NVIDIA GPU Operator support, managed control plane, custom node pools, observability, and built-in security

Cloud compute on AMD EPYC and Intel Xeon processors with NVMe storage, one-click deployments, autoscaling, backups, and scalable VM resources

Cloud storage, S3-compatible object storage, block storage, managed databases, VPC networking, load balancers, floating IPs, firewall/security groups, DDoS protection, and private networking

ISO-certified environments, data sovereignty, VPC isolation, multi-zone redundancy, and a 99.99% uptime SLA

Only charged when your code runs, no charges for cold starts or server spin-up

Memory snapshots and GPU checkpoint restore bring containers back in seconds, which Beam reports as up to 35x faster than a traditional cold boot

Run untrusted code safely in isolated environments for AI agents and code interpreters

Pause and resume sessions while maintaining filesystem, memory, and running processes

Scale to zero when idle, burst to thousands of containers in seconds

Bring your own Docker images for full environment control, including running the Docker daemon inside containers